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Using Commonsense Knowledge to Answer Why-Questions.

, , , , , , and . EMNLP, page 1204-1219. Association for Computational Linguistics, (2022)

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Predicting query performance on the web., , and . SIGIR, page 785-786. ACM, (2010)Syntactic Query Models for Restatement Retrieval., and . SPIRE, volume 5721 of Lecture Notes in Computer Science, page 143-155. Springer, (2009)Beyond Ranked Lists in Web Search: Aggregating Web Content into Topic Pages., and . Int. J. Semantic Comput., 4 (4): 509-534 (2010)Open4Business(O4B): An Open Access Dataset for Summarizing Business Documents., and . CoRR, (2020)PoMo: Generating Entity-Specific Post-Modifiers in Context., , , , , , , and . NAACL-HLT (1), page 826-838. Association for Computational Linguistics, (2019)Using Commonsense Knowledge to Answer Why-Questions., , , , , , and . EMNLP, page 1204-1219. Association for Computational Linguistics, (2022)Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts., , , and . EMNLP, page 6541-6566. Association for Computational Linguistics, (2022)Human Centered NLP with User-Factor Adaptation., , , , and . EMNLP, page 1146-1155. Association for Computational Linguistics, (2017)Latent Part-of-Speech Sequences for Neural Machine Translation., , , , and . EMNLP/IJCNLP (1), page 780-790. Association for Computational Linguistics, (2019)MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance Detection., , , and . EMNLP (Findings), page 2959-2966. Association for Computational Linguistics, (2021)